

import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

__snake_case				:      Dict   		=			logging.getLogger()


def 					_UpperCAmelCase				(						):

			'''simple docstring'''



			a_						:						Optional[int] 							= argparse.ArgumentParser()
			parser.add_argument("""-f""")
			a_						:						int 							= parser.parse_args()
			return args.f


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Dict 							= {}
			a_						:						Dict 							= os.path.join(a__  ,	"""all_results.json""")
			if os.path.exists(a__):
						with open(a__  ,	"""r""") as f:
									a_						:						Dict 							= json.load(a__)
			else:
						raise ValueError(f'''can\'t find {path}''')
			return results


def 					_UpperCAmelCase				(						):

			'''simple docstring'''



			a_						:						Dict 							= torch.cuda.is_available() and torch_device == """cuda"""
			return is_using_cuda and is_apex_available()


__snake_case				:      List[str]   		=			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)



class        A__(a_							):
						"""simple docstring"""






						@classmethod
						def 			UpperCamelCase__					(    cls    )     -> Dict:
									# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
									a_						:						Dict 							= tempfile.mkdtemp()
									a_						:						Dict 							= os.path.join(cls.tmpdir  ,      """default_config.yml"""    )
									write_basic_config(save_location=cls.configPath    )
									a_						:						Union[str, Any] 							= ["""accelerate""", """launch""", """--config_file""", cls.configPath]





						@classmethod
						def 			UpperCamelCase__					(    cls    )     -> Optional[Any]:
									shutil.rmtree(cls.tmpdir    )





						@mock.patch.dict(os.environ  ,      {"""WANDB_MODE""": """offline"""}    )
						def 			UpperCamelCase__					(    self    )     -> Optional[int]:
									a_						:						List[Any] 							= self.get_auto_remove_tmp_dir()
									a_						:						Optional[Any] 							= F'''
            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --seed=42
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

									if is_cuda_and_apex_available():
												testargs.append("""--fp16"""    )

									run_command(self._launch_args + testargs    )
									a_						:						List[Any] 							= get_results(_lowercase    )
									self.assertGreaterEqual(result["""eval_accuracy"""]  ,      0.7_5    )
									self.assertTrue(os.path.exists(os.path.join(_lowercase  ,      """epoch_0"""    )    )    )
									self.assertTrue(os.path.exists(os.path.join(_lowercase  ,      """glue_no_trainer"""    )    )    )





						@mock.patch.dict(os.environ  ,      {"""WANDB_MODE""": """offline"""}    )
						def 			UpperCamelCase__					(    self    )     -> Union[str, Any]:
									a_						:						str 							= self.get_auto_remove_tmp_dir()
									a_						:						Optional[Any] 							= F'''
            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

									if torch.cuda.device_count() > 1:
												# Skipping because there are not enough batches to train the model + would need a drop_last to work.
												return

									run_command(self._launch_args + testargs    )
									a_						:						Optional[Any] 							= get_results(_lowercase    )
									self.assertLess(result["""perplexity"""]  ,      100    )
									self.assertTrue(os.path.exists(os.path.join(_lowercase  ,      """epoch_0"""    )    )    )
									self.assertTrue(os.path.exists(os.path.join(_lowercase  ,      """clm_no_trainer"""    )    )    )





						@mock.patch.dict(os.environ  ,      {"""WANDB_MODE""": """offline"""}    )
						def 			UpperCamelCase__					(    self    )     -> Dict:
									a_						:						List[str] 							= self.get_auto_remove_tmp_dir()
									a_						:						Optional[Any] 							= F'''
            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --num_train_epochs=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

									run_command(self._launch_args + testargs    )
									a_						:						List[Any] 							= get_results(_lowercase    )
									self.assertLess(result["""perplexity"""]  ,      42    )
									self.assertTrue(os.path.exists(os.path.join(_lowercase  ,      """epoch_0"""    )    )    )
									self.assertTrue(os.path.exists(os.path.join(_lowercase  ,      """mlm_no_trainer"""    )    )    )





						@mock.patch.dict(os.environ  ,      {"""WANDB_MODE""": """offline"""}    )
						def 			UpperCamelCase__					(    self    )     -> List[str]:
									# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
									a_						:						Optional[Any] 							= 7 if get_gpu_count() > 1 else 2

									a_						:						Dict 							= self.get_auto_remove_tmp_dir()
									a_						:						Any 							= F'''
            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

									run_command(self._launch_args + testargs    )
									a_						:						Union[str, Any] 							= get_results(_lowercase    )
									self.assertGreaterEqual(result["""eval_accuracy"""]  ,      0.7_5    )
									self.assertLess(result["""train_loss"""]  ,      0.5    )
									self.assertTrue(os.path.exists(os.path.join(_lowercase  ,      """epoch_0"""    )    )    )
									self.assertTrue(os.path.exists(os.path.join(_lowercase  ,      """ner_no_trainer"""    )    )    )





						@unittest.skip(reason="""Fix me @muellerzr"""    )
						@mock.patch.dict(os.environ  ,      {"""WANDB_MODE""": """offline"""}    )
						def 			UpperCamelCase__					(    self    )     -> int:
									a_						:						Dict 							= self.get_auto_remove_tmp_dir()
									a_						:						Optional[Any] 							= F'''
            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --seed=42
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

									run_command(self._launch_args + testargs    )
									a_						:						Optional[int] 							= get_results(_lowercase    )
									# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
									self.assertGreaterEqual(result["""eval_f1"""]  ,      28    )
									self.assertGreaterEqual(result["""eval_exact"""]  ,      28    )
									self.assertTrue(os.path.exists(os.path.join(_lowercase  ,      """epoch_0"""    )    )    )
									self.assertTrue(os.path.exists(os.path.join(_lowercase  ,      """qa_no_trainer"""    )    )    )





						@mock.patch.dict(os.environ  ,      {"""WANDB_MODE""": """offline"""}    )
						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						Optional[int] 							= self.get_auto_remove_tmp_dir()
									a_						:						Tuple 							= F'''
            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=20
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --with_tracking
        '''.split()

									run_command(self._launch_args + testargs    )
									a_						:						Union[str, Any] 							= get_results(_lowercase    )
									self.assertGreaterEqual(result["""eval_accuracy"""]  ,      0.8    )
									self.assertTrue(os.path.exists(os.path.join(_lowercase  ,      """swag_no_trainer"""    )    )    )





						@slow
						@mock.patch.dict(os.environ  ,      {"""WANDB_MODE""": """offline"""}    )
						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						Optional[int] 							= self.get_auto_remove_tmp_dir()
									a_						:						int 							= F'''
            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

									run_command(self._launch_args + testargs    )
									a_						:						List[str] 							= get_results(_lowercase    )
									self.assertGreaterEqual(result["""eval_rouge1"""]  ,      10    )
									self.assertGreaterEqual(result["""eval_rouge2"""]  ,      2    )
									self.assertGreaterEqual(result["""eval_rougeL"""]  ,      7    )
									self.assertGreaterEqual(result["""eval_rougeLsum"""]  ,      7    )
									self.assertTrue(os.path.exists(os.path.join(_lowercase  ,      """epoch_0"""    )    )    )
									self.assertTrue(os.path.exists(os.path.join(_lowercase  ,      """summarization_no_trainer"""    )    )    )





						@slow
						@mock.patch.dict(os.environ  ,      {"""WANDB_MODE""": """offline"""}    )
						def 			UpperCamelCase__					(    self    )     -> Tuple:
									a_						:						Any 							= self.get_auto_remove_tmp_dir()
									a_						:						int 							= F'''
            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
            --model_name_or_path sshleifer/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --num_beams=6
            --learning_rate=3e-3
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

									run_command(self._launch_args + testargs    )
									a_						:						Tuple 							= get_results(_lowercase    )
									self.assertGreaterEqual(result["""eval_bleu"""]  ,      30    )
									self.assertTrue(os.path.exists(os.path.join(_lowercase  ,      """epoch_0"""    )    )    )
									self.assertTrue(os.path.exists(os.path.join(_lowercase  ,      """translation_no_trainer"""    )    )    )





						@slow
						def 			UpperCamelCase__					(    self    )     -> str:
									a_						:						Dict 							= logging.StreamHandler(sys.stdout    )
									logger.addHandler(_lowercase    )

									a_						:						int 							= self.get_auto_remove_tmp_dir()
									a_						:						Tuple 							= F'''
            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
            --dataset_name huggingface/semantic-segmentation-test-sample
            --output_dir {tmp_dir}
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
        '''.split()

									run_command(self._launch_args + testargs    )
									a_						:						Any 							= get_results(_lowercase    )
									self.assertGreaterEqual(result["""eval_overall_accuracy"""]  ,      0.1_0    )





						@mock.patch.dict(os.environ  ,      {"""WANDB_MODE""": """offline"""}    )
						def 			UpperCamelCase__					(    self    )     -> Tuple:
									a_						:						Optional[Any] 							= self.get_auto_remove_tmp_dir()
									a_						:						Optional[Any] 							= F'''
            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
            --model_name_or_path google/vit-base-patch16-224-in21k
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --max_train_steps 2
            --train_val_split 0.1
            --seed 42
            --output_dir {tmp_dir}
            --with_tracking
            --checkpointing_steps 1
        '''.split()

									if is_cuda_and_apex_available():
												testargs.append("""--fp16"""    )

									run_command(self._launch_args + testargs    )
									a_						:						int 							= get_results(_lowercase    )
									# The base model scores a 25%
									self.assertGreaterEqual(result["""eval_accuracy"""]  ,      0.6    )
									self.assertTrue(os.path.exists(os.path.join(_lowercase  ,      """step_1"""    )    )    )
									self.assertTrue(os.path.exists(os.path.join(_lowercase  ,      """image_classification_no_trainer"""    )    )    )





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case				:      int   		=			{
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      int   		=			["""LlamaTokenizer"""]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      int   		=			["""LlamaTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      Union[str, Any]   		=			[
	    """LlamaForCausalLM""",
	    """LlamaModel""",
	    """LlamaPreTrainedModel""",
	    """LlamaForSequenceClassification""",
	]


if TYPE_CHECKING:
	from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama import LlamaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama_fast import LlamaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
	import sys

	__snake_case				:      Dict   		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


